What are people building in computer vision, and what's still painful? [D]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
I've built a lot of ML systems over the years, mainly computer vision models optimised to run on mobile phones. For example, my previous company built the food recognition model for MyFitnessPal.
I'm interested in what people are actually deploying in industry now. Are edge models still a big part of your work, are you hosting your own models, or are you mostly sending requests to APIs? What's driving that choice?
More importantly, what's still a pain? I'd be interested in problems from current or recent projects that existing tools haven't solved well. Something that's cost you a lot of time, blocked delivery or needed an awkward workaround.
I'm looking for problems where I could build useful tooling, rather than guessing what people need. It would also be useful to know where you discuss this stuff or look for help. Are there particular forums or communities worth following?
[link] [comments]
More from r/MachineLearning
-
How can I turn an industry ML project into a publication? [R]
Sep 28
-
Are there any good research papers around Text clustering using LLMs [R]
Sep 28
-
Free, open-source AI engineering course where you build each algorithm by hand: 523 lessons, now as EPUB/PDF books [P]
Sep 28
-
Two-stage shelf audit: YOLO finds the products, embeddings can't tell sibling SKUS apart. What should Stage 2 be? [P]
Sep 27
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.